2 files changed, 64 insertions, 0 deletions
diff --git a/candle-nn/src/lib.rs b/candle-nn/src/lib.rs
index 8e5580df..be95f531 100644
--- a/candle-nn/src/lib.rs
+++ b/candle-nn/src/lib.rs
@@ -11,6 +11,7 @@ pub mod loss;
 pub mod ops;
 pub mod optim;
 pub mod rnn;
+pub mod sequential;
 pub mod var_builder;
 pub mod var_map;
 
@@ -29,6 +30,7 @@ pub use linear::{linear, linear_no_bias, Linear};
 pub use ops::Dropout;
 pub use optim::{AdamW, Optimizer, ParamsAdamW, SGD};
 pub use rnn::{gru, lstm, GRUConfig, LSTMConfig, GRU, LSTM, RNN};
+pub use sequential::{seq, Sequential};
 pub use var_builder::VarBuilder;
 pub use var_map::VarMap;
 
diff --git a/candle-nn/src/sequential.rs b/candle-nn/src/sequential.rs
new file mode 100644
index 00000000..2fef7742
--- /dev/null
+++ b/candle-nn/src/sequential.rs
@@ -0,0 +1,62 @@
+//! A sequential layer used to chain multiple layers and closures.
+use candle::{Module, Result, Tensor};
+
+/// A sequential layer combining multiple other layers.
+pub struct Sequential {
+    layers: Vec<Box<dyn Module>>,
+}
+
+/// Creates a new empty sequential layer.
+pub fn seq() -> Sequential {
+    Sequential { layers: vec![] }
+}
+
+impl Sequential {
+    /// The number of sub-layers embedded in this layer.
+    pub fn len(&self) -> i64 {
+        self.layers.len() as i64
+    }
+
+    /// Returns true if this layer does not have any sub-layer.
+    pub fn is_empty(&self) -> bool {
+        self.layers.is_empty()
+    }
+}
+
+impl Module for Sequential {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let mut xs = xs.clone();
+        for layer in self.layers.iter() {
+            xs = layer.forward(&xs)?
+        }
+        Ok(xs)
+    }
+}
+
+impl Sequential {
+    /// Appends a layer after all the current layers.
+    #[allow(clippy::should_implement_trait)]
+    pub fn add<M: Module + 'static>(mut self, layer: M) -> Self {
+        self.layers.push(Box::new(layer));
+        self
+    }
+
+    /// Appends a closure after all the current layers.
+    pub fn add_fn<F>(self, f: F) -> Self
+    where
+        F: 'static + Fn(&Tensor) -> Result<Tensor> + Send,
+    {
+        self.add(super::func(f))
+    }
+
+    /// Applies the forward pass and returns the output for each layer.
+    pub fn forward_all(&self, xs: &Tensor) -> Result<Vec<Tensor>> {
+        let mut vec = Vec::with_capacity(self.layers.len());
+        let mut xs = xs.clone();
+        for layer in self.layers.iter() {
+            xs = layer.forward(&xs)?;
+            vec.push(xs.clone())
+        }
+        Ok(vec)
+    }
+}